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    Home / Central Data Catalog / UGA_2016_NMPS-ISS_V01_M / variable [F1]
central

National Manpower Survey 2016, Kampala Informal Sector Survey

Uganda, 2016
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Reference ID
UGA_2016_NMPS-ISS_v01_M
DOI
https://doi.org/10.48529/8h55-gf47
Producer(s)
World Bank
Metadata
Documentation in PDF DDI/XML JSON
Created on
Jan 10, 2019
Last modified
Jan 10, 2019
Page views
57102
Downloads
3380
  • Study Description
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  • greaterkampala_finalized_data_2017_02

Raw materials you purchased:2 (T320b__2)

Data file: greaterkampala_finalized_data_2017_02

Overview

Valid: 406
Invalid: 0
Type: Discrete
Start: 5399
End: 5437
Width: 39
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
" baking flour" 1
0.2%
"Maize flour " 1
0.2%
"cooking oil " 1
0.2%
"movit products " 1
0.2%
"sweet potatoes " 1
0.2%
Angle line 1
0.2%
BAKING FLOUR 1
0.2%
BAKING POWDER 1
0.2%
Baking powder 4
1%
Bananas 1
0.2%
Beans 6
1.5%
Bread 2
0.5%
CHARCOAL 3
0.7%
COOKING OIL 9
2.2%
COW PEAS 2
0.5%
Cassava 2
0.5%
Cassava flour 2
0.5%
Chacaol 1
0.2%
Charcoal 10
2.5%
Charcol Stove 1
0.2%
Cheese 1
0.2%
Cigarates 1
0.2%
Condiments 2
0.5%
Cooking Oil 4
1%
Cooking oil 9
2.2%
Cosmetics 1
0.2%
Costic acid 1
0.2%
Cow peas 1
0.2%
Curry powder 1
0.2%
DRINKS 1
0.2%
DRUG 1
0.2%
DYE 1
0.2%
Darling weaves 1
0.2%
Diesel 1
0.2%
EGGS 1
0.2%
Egg plants 1
0.2%
Eggs 5
1.2%
Energy drinks 1
0.2%
Engano 1
0.2%
Eye drop 1
0.2%
FIRE WOOD 1
0.2%
GAM 1
0.2%
GLUE 1
0.2%
GREENPEPPER 1
0.2%
Glue 2
0.5%
Green pepper 2
0.5%
Irish Potatoes 1
0.2%
Irish potatoes 1
0.2%
Irish pototoes 1
0.2%
Iron plates 1
0.2%
Layers 1
0.2%
Leather 1
0.2%
MATOOKE 1
0.2%
MEAT 2
0.5%
METALIC FLAMES 1
0.2%
Maize flour 1
0.2%
Mangoes 1
0.2%
Matoke 1
0.2%
Meat 2
0.5%
Medicine 1
0.2%
Metal 1
0.2%
Mineral water 1
0.2%
Nails 1
0.2%
Nakati 1
0.2%
Newcastle 1
0.2%
Niddles 1
0.2%
OIL 1
0.2%
ONIONS 4
1%
Oil 2
0.5%
Onions 9
2.2%
Other ingredients 1
0.2%
PORK MEAT 1
0.2%
POSH 2
0.5%
PREMIX AND POULTRY CARE 1
0.2%
Package material 1
0.2%
Paint 1
0.2%
Palm 1
0.2%
Pipes 1
0.2%
Plumb bob 1
0.2%
Posho 4
1%
RICE 2
0.5%
RUBBER BAND 1
0.2%
Rice 3
0.7%
SALT 1
0.2%
SHELLS 1
0.2%
SOLES 1
0.2%
SORGUM 2
0.5%
SUGAR 1
0.2%
SULPHONIC ACID 1
0.2%
SUN PAPER 1
0.2%
Salt 11
2.7%
Sand 1
0.2%
Sand paper 1
0.2%
Soap 1
0.2%
Spices 5
1.2%
Spirits 1
0.2%
Spray 1
0.2%
Sugar 6
1.5%
Sunflower 1
0.2%
TCB Treatment 1
0.2%
TOMATOES 2
0.5%
TRANSPORT 1
0.2%
TV stands 1
0.2%
Tea leaves 1
0.2%
Threads 1
0.2%
Tomatoes 6
1.5%
Tools & equipment 1
0.2%
Toothpaste 1
0.2%
VANISH 2
0.5%
VEGETABLES 2
0.5%
VEGETABLES ( NAKATI, ONIONS, TOMATOES ) 1
0.2%
Vanish 1
0.2%
Vernish 1
0.2%
Vitamin 1
0.2%
Vitamins 1
0.2%
WATER 3
0.7%
Water 1
0.2%
Wheat flour 2
0.5%
Wood Glue 1
0.2%
baking flour 1
0.2%
baking Flour 1
0.2%
baking flour 3
0.7%
baking powder 5
1.2%
banana 1
0.2%
bananas/matooke 1
0.2%
beans 11
2.7%
boards 1
0.2%
bread 1
0.2%
bukuta 1
0.2%
carrot 2
0.5%
carrots 1
0.2%
carry power 1
0.2%
cassava 3
0.7%
charcoal 13
3.2%
chickens 1
0.2%
clothes 1
0.2%
clothes material 1
0.2%
colour 1
0.2%
cooking 1
0.2%
cooking oil 23
5.7%
cotton cake 1
0.2%
cowleg 1
0.2%
cows meat 1
0.2%
eggs 11
2.7%
firewood 2
0.5%
fish 1
0.2%
flour 2
0.5%
fresh chicken 1
0.2%
fuel 1
0.2%
ground nuts 2
0.5%
gum 1
0.2%
hair waives 1
0.2%
lastic 1
0.2%
maize 1
0.2%
maize flour 2
0.5%
maize grains 1
0.2%
maize seeds 1
0.2%
matooke 1
0.2%
meat 4
1%
milk 1
0.2%
nails 1
0.2%
needles 1
0.2%
oil 1
0.2%
oil for retouch 1
0.2%
oils 2
0.5%
onion 1
0.2%
onions 7
1.7%
paint 1
0.2%
paints 1
0.2%
papers 1
0.2%
peas 2
0.5%
posha 1
0.2%
posho 1
0.2%
rice 5
1.2%
salt 7
1.7%
samona 1
0.2%
sand siller 1
0.2%
shampoo 1
0.2%
shoe repairing tools 1
0.2%
soda 3
0.7%
sodas 1
0.2%
sole 1
0.2%
spices 1
0.2%
spirits 1
0.2%
spirts 1
0.2%
stainless hole section 1
0.2%
strings 1
0.2%
sugar 2
0.5%
suger 1
0.2%
sulphoric acid 1
0.2%
supergate 1
0.2%
sweet banana 1
0.2%
tea leaves 1
0.2%
threads 3
0.7%
tomatoes 3
0.7%
tomotoes 1
0.2%
treatments 1
0.2%
utilising sampo 1
0.2%
vanish 1
0.2%
vegetables 2
0.5%
washing detergent 1
0.2%
water 2
0.5%
wheat 1
0.2%
wheat flour 4
1%
yeast 1
0.2%
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.
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